genoplot: Genome plot of the eQTL data on the expression traits...

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/genoplot.R

Description

Plots the estimated eQTL positions with the genomic positions of the controlled gene.

Usage

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genoplot( peak.array, cross, etrait.coord, data.gmap, chr.size, save.pict=FALSE, ...)

Arguments

cross

An object of class cross. See 'qtl' package manual for read.cross function details.

peak.array

An object of class peak.array. See peak.2.array function for details.

etrait.coord

A data frame specifying the etrait genomic locations with columns:
etrait.name a factor with array element or gene name as levels.
chr an integer vector determining the chromosome.
start an integer vector determining the GST start location in base pair.
stop an integer vector determining the GST stop location in base pair.

data.gmap

A data frame with column names "Marker", "chr" and "PP" specifying the marker physical locations. Those one must be the same markers defined in the related cross object. data.gmap\$Marker is a vector character strings specifying the names of markers.
data.gmap\$chr is a vector of integers specifying the chromosome on which the markers are localized.
data.gmap\$PP is a vector of integers specifying the physical marker location on the chromosome in base pair.

chr.size

A vector of integer specifying the size of the chromosomes in base pair in order of the chromosomes.

save.pict

If TRUE, save each charts generated by genoplot as png files in the current folder.

...

Ignored at this step.

Details

Useful for genetical genomics studies. This function gives a graphical overview of the global eQTL network by plotting the estimated eQTL positions with the genomic positions of the affected traits. Six charts are generated and all locations data are represented on a physical scale. The genomic ditribution of both affected traits and QTLs are described by two histograms. If save.pict=TRUE, these histograms are saved as ‘./histogram\_controled\_gst.png’ and ‘./histogram\_qtl.png’ files, respectively. The etrait~eQTL plot are represented with LOD color scale (from green to red in order of increasing LOD score, blue representing the average LOD SCORE) and with additive effect color scale (from green to red in order of increasing additive effect, yellow representing the null additive effect). Four etrait~eQTL plot are generated representing the eQTL locations as single LOD peaks or support interval regions, both with LOD and additive effect color scales. If save.pict=TRUE, these plot are saved as ‘lod\_dotplot\_traitxqtl.png’, ‘ae\_dotplot\_traitxqtl.png’, ‘lod\_siplot\_traitxqtl.png’ and ‘ae\_siplot\_traitxqtl.png’ files.

Value

return a list with elements:

coord_etrait

the etrait coordinates.

coord_qtl

the QTL coordinates.

limit

the chromosomes limits.

add_etrait

the cumulates size of the chromosomes in bp for the etrait.

add_qtl

the cumulates size of of the chromosomes in bp for the QTL.

Author(s)

Hamid A. Khalili

See Also

define.peak,read.cross

Examples

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data(seed10);

seed10 <- calc.genoprob( cross=seed10, step=2, off.end=0, error.prob=0,
	 map.function='kosambi', stepwidth='fixed');
seed10 <- sim.geno( cross=seed10, step=2, off.end=0, error.prob=0,
	 map.function='kosambi', stepwidth='fixed');

out.em <- scanone( seed10, pheno.col=1:50, model='normal', method='hk');
out.peak <- define.peak( out.em, 'all');
out.peak <- calc.adef(seed10,out.em,out.peak);

data(BSpgmap);
data(ATH.coord);

out.peak <- localize.qtl(seed10, out.peak, BSpgmap);
out.array <- peak.2.array(out.peak)

genoplot(out.array, seed10, ATH.coord, BSpgmap,
	 chr.size=c(30432457,19704536,23470536,18584924,26991304), save.pict=TRUE);
# NB: the size of the Arabidopsis thaliana chromosomes are
# 30432457, 19704536, 23470536, 18584924 and 26991304 total base pairs
# for chromosomes 1 to 5 respectively

Example output

Loading required package: qtl
no. of traits: 50
trait: ID	lodcolumn: 1
trait: CATcmt	lodcolumn: 2
trait: CATmjm	lodcolumn: 3
trait: CATppt	lodcolumn: 4
trait: CATvvr	lodcolumn: 5
trait: CATxni	lodcolumn: 6
trait: CATfgb	lodcolumn: 7
trait: CATqoq	lodcolumn: 8
trait: CATsgu	lodcolumn: 9
trait: CATwwh	lodcolumn: 10
trait: CATpxm	lodcolumn: 11
trait: CATkks	lodcolumn: 12
trait: CATbaa	lodcolumn: 13
trait: CATham	lodcolumn: 14
trait: MITO312	lodcolumn: 15
trait: CATudg	lodcolumn: 16
trait: CATwgo	lodcolumn: 17
trait: CATdbn	lodcolumn: 18
trait: CATipy	lodcolumn: 19
trait: CATpyn	lodcolumn: 20
trait: CATfnu	lodcolumn: 21
trait: CATjoj	lodcolumn: 22
trait: CATpsl	lodcolumn: 23
trait: CATntj	lodcolumn: 24
trait: CATjwf	lodcolumn: 25
trait: CATrck	lodcolumn: 26
trait: CATane	lodcolumn: 27
trait: CAToti	lodcolumn: 28
trait: CATbzx	lodcolumn: 29
trait: CATrjo	lodcolumn: 30
trait: CATjxh	lodcolumn: 31
trait: CATlkr	lodcolumn: 32
trait: CATnrd	lodcolumn: 33
trait: CATtez	lodcolumn: 34
trait: CATjpz	lodcolumn: 35
trait: CATbko	lodcolumn: 36
trait: CATavz	lodcolumn: 37
trait: CATvkm	lodcolumn: 38
trait: CATuhl	lodcolumn: 39
trait: CATlbg	lodcolumn: 40
trait: CATvbb	lodcolumn: 41
trait: CATowo	lodcolumn: 42
trait: CATlzh	lodcolumn: 43
trait: CATepg	lodcolumn: 44
trait: CATbuu	lodcolumn: 45
trait: CATwrn	lodcolumn: 46
trait: CATtgk	lodcolumn: 47
trait: CATxmc	lodcolumn: 48
trait: CATdmq	lodcolumn: 49
trait: CATbzp	lodcolumn: 50
define.peak in process...
peaks features:
 [1] "lod"             "mname.peak"      "peak.cM"         "mname.inf"      
 [5] "inf.cM"          "mname.sup"       "sup.cM"          "si.quality"     
 [9] "additive.effect" "peak.bp"         "inf.bp"          "sup.bp"         

[1] "chromosome size:"
[1]     0.00 30432.46 19704.54 23470.54 18584.92 26991.30
[1] "cumul:"
[1]      0.00  30432.46  50136.99  73607.53  92192.45 119183.76
26.89773 2.314713 
1.00261 -1.187079 
$coord_gst
 [1]  8720.609  8720.609  8720.609   942.187  1475.018  1475.018 11774.705
 [8] 21928.811 21928.811 21928.811 20896.379  3789.822  6021.201  6021.201
[15]   286.199   286.199 10351.938 10351.938   935.942   935.942 13494.665
[22] 29290.554 25921.385 25921.385  6858.294  7099.383 16308.151 16439.598
[29] 21203.945  2171.654 19067.570 19067.570  9668.429 24358.439 13547.744
[36] 19701.814 11487.254 16732.502 19252.182 16102.782 16102.782 16102.782
[43] 17379.638  8863.137  8863.137 12686.618 12686.618  1227.614  8200.967
[50]  8200.967  8200.967  8200.967 29091.044 20629.801 20629.801 19787.339
[57] 17884.051

$coord_qtl
 [1]    89.4980   905.5649   699.9895 23024.2033  1404.1199  1507.1040
 [7]   905.5649   407.0100 22442.8003 24884.7764   905.5649   905.5649
[13]   905.5649  1036.2872    89.4980  1404.1199   905.5649  1036.2872
[19] 12810.5181 18406.0660  1404.1199  1404.1199   905.5649  1036.2872
[25]  6905.9975  1404.1199   905.5649  1404.1199   905.5649  1036.2872
[31]    89.4980  1404.1199   905.5649 24374.0080   905.5649   905.5649
[37]   905.5649   905.5649   905.5649    89.4980   905.5649   699.9895
[43]    89.4980    89.4980   905.5649    89.4980   905.5649   905.5649
[49]  8192.9510   905.5649  7900.2219  6036.5479  2401.2298  9570.1792
[55] 14276.4258   905.5649   407.0100

$limit
[1]      0.00  30432.46  50136.99  73607.53  92192.45 119183.76

$add_gst
 [1] 73607.53 73607.53 73607.53     0.00 50136.99 50136.99     0.00 92192.45
 [9] 92192.45 92192.45 92192.45 92192.45 73607.53 73607.53     0.00     0.00
[17] 30432.46 30432.46 30432.46 30432.46 50136.99     0.00 92192.45 92192.45
[25] 30432.46 50136.99 92192.45     0.00 92192.45     0.00 92192.45 92192.45
[33] 92192.45     0.00 92192.45 92192.45 73607.53     0.00 50136.99 73607.53
[41] 73607.53 73607.53 73607.53 50136.99 50136.99 73607.53 73607.53 73607.53
[49]     0.00     0.00     0.00     0.00     0.00 92192.45 92192.45 92192.45
[57]     0.00

$add_qtl
 [1] 73607.53 73607.53 92192.45 92192.45 73607.53 92192.45 73607.53 73607.53
 [9] 92192.45 92192.45 73607.53 73607.53 73607.53 92192.45 73607.53 73607.53
[17] 73607.53 92192.45     0.00 50136.99 73607.53 73607.53 73607.53 92192.45
[25] 30432.46 73607.53 73607.53 73607.53 73607.53 92192.45 73607.53 73607.53
[33] 73607.53     0.00 73607.53 73607.53 73607.53 73607.53 73607.53 73607.53
[41] 73607.53 92192.45 73607.53 73607.53 73607.53 73607.53 73607.53 73607.53
[49]     0.00 73607.53 73607.53 92192.45 73607.53 30432.46 30432.46 73607.53
[57] 73607.53

eqtl documentation built on May 29, 2017, 6:38 p.m.

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